Quantitative system performance: computer system analysis using queueing network models
Quantitative system performance: computer system analysis using queueing network models
A central-limit-theorem version of L=λW
Queueing Systems: Theory and Applications
Ordinary CLT and WLLN versions of L=λW
Mathematics of Operations Research
Operations Research
Simulation run lengths to estimate blocking probabilities
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Open, Closed, and Mixed Networks of Queues with Different Classes of Customers
Journal of the ACM (JACM)
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Variance Reduction in Simulations of Loss Models
Operations Research
Efficient simulation of queues in heavy traffic
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Optimizing system configurations quickly by guessing at the performance
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Relations in the Central Limit Theorem Version of the Response Time Law
QEST '07 Proceedings of the Fourth International Conference on Quantitative Evaluation of Systems
| Hi-index | 0.00 |
The system of measuring the performance of a Web system using a workload generator can be modeled as a closed interactive system. In such a system, the throughput and the mean response time are related by the response time law. However, we find that a measured throughput and a corresponding measured mean response time can have significantly different accuracy. As a result, one metric may be more reliable than the other to identify the better of two given configurations of a Web system, which is an important problem that appears frequently in practice. Using simulation, we derive rules of thumb that characterize when throughput is more reliable than mean response time. Also, we explain these rules of thumb analytically. Specifically, we refine the response time law using the central limit theorem and formally define the asymptotic reliability of an estimator of a metric. Using these analytical frameworks, we provide insights into when and why one metric is more reliable than the other.